Internationalisation of State Owned
Enterprises: An Institutional View
Saul Estrin
Department of Management
London School of Economics
Presentation for UCL Workshop, ‘Transition Economics meets
New Structural Economics’, June 25/26 2013
1
• Paper joint with:
• Klaus Meyer, CEIBS, Shanghai, China
• Bo Nielsen, Copenhagen Business School
• Sabina Nielsen, Copenhagen Business School
2
Outline
• Theoretical framework
• State ownership and internationalisation
• Benevolent( “Helping hand”) and non-benevolent (“grabbing hand”)
managerial motives
• Institutions and State ownership
• Hypotheses
• Informal institutions
• Formal institutions
• Governance
• Data
• Results
• Conclusions
3
Outward FDI Flows Worldwide 1970 - 2010
[Source: Created based on data obtained from UNCTAD FDI database]
FDI Stock 1980 to 2010
20%
Other Emerging
100%
South Africa
18%
Developed
90%
Russia
NIE
16%
14%
Other Emerging
BRICS
India
80%
China
70%
Brazil
Taiwan (RoC)
60%
12%
Singapore
50%
10%
Korea (Rep)
Hong Kong
40%
8%
Other developed
30%
6%
Japan
France
20%
4%
Germany
10%
UK
2%
0%
1980
0%
1980 1985 1990 1995 2000 2005 2010
1985
1990
1995
2000
2005
2010
USA
State Ownership and
Internationalisation
• Theories of internationalisation focus on firm specific resources
and capabilities
• As share of FDI from developed economies declines, increasing
heterogeneity of organisation forms and ownership; i.e. state
ownership of enterprises (SOEs)
• Research question: How does state ownership impact on firm’s
internationalisation?
– SOEs differ from private firms in objectives, attitudes to risk and access to
resources
– Impact of these differences influenced by specifics of local institutional
environment.
– Build on Williamson, 2000 hierarchy of institutions – informal, formal,
corporate governance.
6
Institutions, State Ownership and
Internationalisation
• Institutions affect firm’s allocation of resources to international operations
in both markets and hierarchies (SOEs).
 Institutions constrain decision makers in SOEs by limiting ability of insiders to
exploit SOE for own objectives
 The more that institutions constrain leaders in SOEs, the more likely that SOEs
pursue ‘benevolent’ objectives and their internationalisation strategies
resemble those of private firms.
• Very little literature on SOEs and internationalisation; Mazzolini 1979,
Vernon 1979 and recent work on China (Ramasamy et al 2012, Wang
2012)
• Two recent papers on other countries:
– Spain (Garcia-Canul and Gwellen, 2008)
– Norway (Knutsen et al 2011)
• No work on how institutions moderate internationalisation of SOEs
7
SOEs, Institutions and
Internationalisation
• Distinguish ‘benevolent’ (Helping hand) contribution of SOEs to society
and non –benevolent “grabbing hand” (Shleifer and Vishny, 1998)
• Benevolent impact depends on political economy and institutions e.g.
– in liberal economies, SOEs address market failures
– in social democracies, SOEs redistribute income
– in state capitalist and transition economies, SOEs guide economic development
• Grabbing hand
– SOEs used by politicians to exploit state apparatus in pursuit of personal and political
gain.
– Used by managers to extract rents
• If benevolent motives dominate, SOE acts like private firms, perhaps with
certain objectives added
• If non-benevolent motives dominate, SOEs used to extract rents, which
are usually found domestically
8
Institutions and SOEs
• The extent SOEs follow benevolent or nonbenevolent motives depends on local institutional
context
– Private firms benefit from good institutions at all
levels – increase resources available and enhance
internationalisation strategies
– Same applies to SOEs – inspire monitoring and
shift balance in favour of benevolent managerial
strategies
– Hence interactions with institutions moderate
SOE strategies to internationalise
9
Figure 1: Theoretical Framework derived from
Williamson (2000)
Informal
institutions
State
Ownership
Formal
institutions
Governance
institutions
Allocation of resources to
international operations
10
Hypothesis 1: Informal Institutions
• Informal institutions represent a cultural filter that provides continuity
(North, 1990). Not prone to deliberate change (Hofstede, 1991).
• Most relevant concept is Hofstede’s 1980 “power distance” – “extent to
which less powerful members of organisations … accept and expect
power is distributed unequally”.
• High power distance makes organisations less entrepreneurial and
competitive in global markets. Decision makers less likely to be
challenged. Also discourages risk taking e.g. internationalisation.
• High power distance places the leader in greater authority – in SOEs, more
likely to use power to pursue political interest and rent extraction.
H1: Informal institution of power distance will have a negative
effect on the relationship between state ownership and
internationalisation.
11
Hypothesis 2: Formal institutions
• Operating in vaguely defined or weakly enforced legal
framework reduces constraints on self serving objectives,
especially in SOEs.
• Weak formal institutions raise importance of non-market
capabilities, i.e. lobbying, government relationship etc. These
cannot be easily leveraged into foreign markets.
• Hence, strong institutions more likely to ensure SOEs have
benevolent rather than non-benevolent motives.
H2: Better formulated and enforced institutions of the rule of
law will have a positive effect on the relationship between
state ownership and internationalisation
12
Hypothesis 3: Governance
• Effectiveness of domestic capital markets has greatest effect
on behaviour of firms. Acts as benchmarks to assess
performance and in aligning managerial and investor
objectives.
• Listed SOEs subject to state and (minority) private owners.
• Deviations towards non-benevolent objectives, which militate
against internationalisation, are harder to conceal and to
implement as capital market institutions improve.
H3: Higher levels of development of the governance institution
of capital markets will have a positive effect on the relationship
between state ownership and internationalisation.
13
Figure 2: Framework for Empirical Testing
Informal
institutions
State
Ownership
H1
Formal
institutions
H2
Governance
institutions
H3
Internationalization
14
Data
• Sample from Worldscope, for world’s largest 5000 firms based
on sales in 2010.
• Thomson One Banker used for firm level data, except
ownership data from Orbis.
• Country level data from World Bank and Hofstede, 2001.
• Sample contains 153 SOEs (firm with >50% equity held by
state). For example from the Top 100 in 2010:
– There were ten Chinese firms, 9 were SOEs
– There were two Brazilian firms, both were SOEs
• Matched sample created from the remaining 4847 firms by
size (sales) and industry (SIC)
15
Table 1: Sample Matched by Industry and Size
Country
Argentina
Australia
Austria
Belgium
Bahrain
Brazil
Canada
Switzerland
China
Germany
Finland
France
United
Kingdom
Greece
Hong Kong
Indonesia
India
Ireland
Israel
Italy
POE
0
2
0
1
1
1
3
3
3
10
2
4
SOE
1
1
1
2
0
8
0
2
36
2
1
4
Total
1
3
1
3
1
9
3
5
39
12
3
8
7
0
2
2
4
0
2
7
0
2
8
5
36
1
0
2
7
2
10
7
40
1
2
9
Country
Japan
Kuwait
Malaysia
Holland
Norway
Pakistan
Philippines
Poland
Portugal
Qatar
Russia
Saudi Arabia
POE
25
0
0
1
0
0
3
0
1
0
5
1
SOE
1
1
3
0
3
2
0
3
0
2
15
3
Tota
l
26
1
3
1
3
2
3
3
1
2
20
4
Singapore
Sweden
Thailand
Turkey
Taiwan
USA
South Africa
United Arab Emirates
Total
1
4
1
1
3
51
2
0
153
3
1
2
0
0
0
0
2
153
4
5
3
1
3
51
2
2
306
16
Measures
• Dependent variable: foreign assets/total sales
• Focal variable: State ownership: defined as whether a state
entity controls more than 50% of the firms’s equity
• Moderating variables:
– Informal institutions (power distance) measure by Hofstede’s cultural
dimension score
– Formal institutions measure by “rule of law” from Worldwide
Governance Indicators (Kaufman et al 2010)
– Governance institutions by capital market development, i.e. share
price x number of shares outstanding as percentage of GDP (World
Bank 2010).
• Estimated using Tobit because left-centred.
17
Control Variables
• Country level
– GDP per capita ($)
• Industry level (3-digit SIC)
– Industry concentration (“C4”)
– Industry growth (%)
– Resource based industries (SIC < 1500).
• Firm level
– Product diversification (entropy measure)
– size (log employees)
– R&D expenditures (% of sales)
VIF and correlation tables shows high interdependency between
institutional variables so included separately and with
interaction.
Descriptive statistics and correlations
Variable
Internationalization
State ownership
Mean
0.13
0.5
St.d.
0.21
0.5
1
1
-.18
R&D intensity
0.59
1.72
.21
R&D presence
0.44
0.5
.15
Firm size
Product diversification
9.13
0.73
1.51
0.52
.10
.11
45.38
40.91
.11
Industry growth
0.03
0.06
-.09
Industry concentration
0.63
0.22
.09
.05
.12
0.1
685.9
7
0.3
.05
.13
898.06
-.20
.23
Power distance
62.2
20.03
-.21
Rule of law
Capital market
development
0.69
103.8
4
0.93
.33
80.4
.19
9.58
1.33
.33
.59
.55
.01
.56
Firm age
Resource-based industry
Currency reserves
GDP
per capita
a Currency
reserves is in $ billions.
All correlations = .11 or above are significant at p<.05
2
3
1
.22
.14
1
.19
.10
.12
.39
.01
.05
.13
.04
4
1
.11
.04
.05
.08
5
6
1
.17
1
.14
.06
.05
.08
.20
.04
.09
.09
.03
.09
.04
.03
.06
.17
.18
.14
.03
.10
.22
.06
.15
.10
.21
.12
.09
.05
.11
.09
.03
.04
.07
.02
7
1
.06
.09
.12
.26
.25
8
9
1
.14
.27
1
.01
.23
.03
.20
.27
.01
.00
.20
.01
.03
.13
.00
.22
.11
10
11
12
13
14
15
1
.08
1
.12
.19
.32
.38
.12
.27
.02
.13
1
.83
.09
.72
1
.31
1
.83
.17
1
19
Results
• H1 supported - Positive significant moderating
influence of power distance on
internationalisation of SOEs
• H2 supported – positive significant
moderating influence of rule of law on
internationalisation of SOEs
• H3 supported – positive significant moderates
impact of market capitalisationon
internationalisation of SOEs
20
Estimation of Tobit regressions for internationalization
Variable
Model 1
Intercept
R&D intensity
R&D presence
Model 2
Product diversification
Firm age
Industry growth
Industry concentration
Currency reserves
GDP per capita
State ownership
Model 5
Model 6
Model 7
***
-1.19 (0.28)
***
-1.15 (0.27)
***
-0.66 (0.32)
*
-0.56 (0.32)
+
-1.06 (0.23)
***
-1.04 (0.23)
***
0.03 (0.01)
**
0.03 (0.01)
**
0.03 (0.01)
**
0.03 (0.01)
**
0.03 (0.01)
**
0.03 (0.01)
**
0.03 (0.01)
**
0.02 (0.05)
0.03 (0.05)
0.01 (0.05)
0.02 (0.05)
0.04 (0.05)
0.05 (0.04)
0.03 (0.01)
*
0.04 (0.01)
*
0.04 (0.01)
**
0.03 (0.01)
*
0.04 (0.01)
**
0.04 (0.01)
**
0.04 (0.01)
**
0.11 (0.04)
**
0.12 (0.04)
**
0.11 (0.04)
**
0.11 (0.04)
**
0.10 (0.04)
**
0.12 (0.04)
**
0.11 (0.04)
**
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.06 (0.37)
0.06 (0.38)
0.09 (0.37)
0.01 (0.36)
0.04 (0.36)
0.02 (0.36)
0.05 (0.35)
-0.13 (0.10)
Resource-based industry
Model 4
-1.14 (0.24)
0.03 (0.05)
Firm size
Model 3
-0.13 (0.10)
-0.10 (0.10)
-0.14 (0.10)
-0.14 (0.10)
-0.12 (0.10)
-0.11 (0.09)
0.19 (0.07)
**
0.20 (0.07)
**
0.19 (0.07)
**
0.21 (0.07)
**
0.20 (0.07)
**
0.17 (0.07)
*
0.18 (0.07)
*
-0.00 (0.00)
***
-0.00 (0.00)
**
-0.00 (0.00)
*
-0.00 (0.00)
*
-0.00 (0.00)
+
-0.00 (0.00)
**
-0.00 (0.00)
**
0.09 (0.02)
***
0.09 (0.02)
***
0.08 (0.02)
***
***
0.07 (0.02)
**
-0.05 (0.05)
Informal (Power distance)
-0.08 (0.05)
-0.08 (0.05)
0.00 (0.00)
0.00 (0.00)
*
-0.01 (0.00)
*
Informal (Power distance)
* SOE
Formal (Rule of law)
0.04 (0.03)
0.03 (0.05)
0.07 (0.02)
-0.04 (0.05)
-0.06 (0.05)
-0.07 (0.05)
0.09 (0.04)
*
Formal (Rule of law)
* SOE
-0.07 (0.05)
0.03 (0.05)
0.14 (0.05)
**
Governance (Market based)
0.00 (0.00)
**
Governance (Market based)
* SOE
0.00 (0.00)
N
301
286
286
301
301
301
300
2
87.2
80.29
86.08
91.72
98.84
96.68
104.43
Log-likelihood
McFadden's pseudo R2 a
a
-0.00 (0.00)
R2 as
***
***
***
***
***
***
-112.96
-107.04
-104.15
-110.7
-107.14
-108.22
-103.69
0.28
0.27
0.29
0.29
0.32
0.31
0.33
McFadden's pseudo
Tobit regression does not have an equivalent to the R-squared that is found in OLS regression.
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001
21
**
***
Robustness Checks
• Results hold when continuous measure of State ownership
used
• Results hold when alternative proxies for hypotheses used
e.g. IPR rather than rule of law; Globe measures of power
distance
• Results hold with additional controls e.g. for India and China;
listing on stock exchanges overseas
• Results hold on estimates for full model as well as matched
sample.
22
Implications: Power Distance
In general, we find that the more that institutions provide for
effective controls over decision makers, the more that SOEs
pursue internationalisation strategies in the same way as private
firms. Hence:
• As power distance within a country increases, SOEs
internationalise less and private firms more - see table 3.
• Suggests “managerial hubris” for private firms and private
benefits at home for SOEs.
23
.05
.1
Figure 3: Interaction of State Ownership and Power Distance
-.1
-.05
0
Linear
Prediction
42
62
Power Distance
POE
82
SOE
24
Implications Ctd.: Rule of Law and
Capital Markets
• Strong institutions at these two levels propel
SOEs to internationalise.
• However effect on private firms of stronger
institutions is these areas is small.
• Hence improvements in institutional
arrangements reduce the gap between these
types of firm.
25
-.2
Linear
-.1 Prediction0
.1
.2
Figure 4: Interaction of State Ownership and Rule of Law
-.242
.687
1.617
Rule of Law
POE
SOE
26
.05
0
-.15
-.1
-.05
Linear Prediction
Figure 5: Interaction of State Ownership and Capital Markets
23.4
103.8
184.2
Capital Markets
POE
SOE
Note to Figures 3 to 5: The horizontal axis shows the range of the explanatory variables from one standard deviation below the mean to one
standard deviation above the mean.
27
Conclusions
• SOEs have emerged as important international players.
However their role and impact are strongly influenced by
home country institutions
• Lower power distance, highly developed legal systems and
high levels of capital market development reduce home bias
of SOEs.
• For policy makers, higher level institutions change very slowly
if at all. Immediate effects likely to be left via governance
institutions.
28